Locality mapping in a distributed processing system

ABSTRACT

Topology mapping in a distributed processing system that includes a plurality of compute nodes, including: initiating a message passing operation; including in a message generated by the message passing operation, topological information for the sending task; mapping the topological information for the sending task; determining whether the sending task and the receiving task reside on the same topological unit; if the sending task and the receiving task reside on the same topological unit, using an optimal local network pattern for subsequent message passing operations between the sending task and the receiving task; otherwise, using a data communications network between the topological unit of the sending task and the topological unit of the receiving task for subsequent message passing operations between the sending task and the receiving task.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of and claims priorityfrom U.S. patent application Ser. No. 12/985,075, filed on Jan. 5, 2011.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for topology mapping in a distributedprocessing system.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Modern computing systems can include a plurality of compute nodes thatare each running tasks that communicate with each other. Each task isnot necessarily aware of the topology of the computing system. Forexample, each task may not know which other tasks are running on thesame compute node, which tasks are running on other compute nodes, andso on.

SUMMARY OF THE INVENTION

Methods, apparatus, and products for topology mapping in a distributedprocessing system, the distributed processing system including aplurality of compute nodes, each compute node having a plurality oftasks, including: initiating, by a sending task, a message passingoperation that is unrelated to topology mapping with a receiving task;including, by the sending task, in a message generated by the messagepassing operation topological information for the sending task; mapping,by the receiving task, the topological information for the sending task;determining whether the sending task and the receiving task reside onthe same topological unit; if the sending task and the receiving taskreside on the same topological unit, using an optimal local networkpattern for subsequent message passing operations between the sendingtask and the receiving task; and if the sending task and the receivingtask do not reside on the same topological unit, using a datacommunications network between the topological unit of the sending taskand the topological unit of the receiving task for subsequent messagepassing operations between the sending task and the receiving task.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of example embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of example embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 sets forth example apparatus for topology mapping in adistributed processing system according to embodiments of the presentinvention.

FIG. 2 sets forth a block diagram of an example compute node useful in aparallel computer capable of topology mapping in a distributedprocessing system according to embodiments of the present invention.

FIG. 3A sets forth a block diagram of an example Point-To-Point Adapteruseful in systems for topology mapping in a distributed processingsystem according to embodiments of the present invention.

FIG. 3B sets forth a block diagram of an example Global CombiningNetwork Adapter useful in systems for topology mapping in a distributedprocessing system according to embodiments of the present invention.

FIG. 4 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful insystems capable of topology mapping in a distributed processing systemaccording to embodiments of the present invention.

FIG. 5 sets forth a line drawing illustrating an example globalcombining network useful in systems capable of topology mapping in adistributed processing system according to embodiments of the presentinvention.

FIG. 6 sets forth a flow chart illustrating an example method fortopology mapping in a distributed processing system according toembodiments of the present invention.

FIG. 7 sets forth a flow chart illustrating a further example method fortopology mapping in a distributed processing system according toembodiments of the present invention.

FIG. 8 sets forth a flow chart illustrating a further example method fortopology mapping in a distributed processing system according toembodiments of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Example methods, apparatus, and products for topology mapping in adistributed processing system in accordance with the present inventionare described with reference to the accompanying drawings, beginningwith FIG. 1. FIG. 1 sets forth example apparatus for topology mapping ina distributed processing system according to embodiments of the presentinvention. The apparatus of FIG. 1 includes a parallel computer (100),non-volatile memory for the computer in the form of a data storagedevice (118), an output device for the computer in the form of a printer(120), and an input/output device for the computer in the form of acomputer terminal (122). The parallel computer (100) in the example ofFIG. 1 includes a plurality of compute nodes (102). The compute nodes(102) are coupled for data communications by several independent datacommunications networks including a high speed Ethernet network (174), aJoint Test Action Group (‘JTAG’) network (104), a global combiningnetwork (106) which is optimized for collective operations using abinary tree network topology, and a point-to-point network (108), whichis optimized for point-to-point operations using a torus networktopology. The global combining network (106) is a data communicationsnetwork that includes data communications links connected to the computenodes (102) so as to organize the compute nodes (102) as a binary tree.Each data communications network is implemented with data communicationslinks among the compute nodes (102). The data communications linksprovide data communications for parallel operations among the computenodes (102) of the parallel computer (100).

The compute nodes (102) of the parallel computer (100) are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on the parallel computer (100). Eachoperational group (132) of compute nodes is the set of compute nodesupon which a collective parallel operation executes. Each compute nodein the operational group (132) is assigned a unique rank that identifiesthe particular compute node in the operational group (132). Collectiveoperations are implemented with data communications among the computenodes of an operational group. Collective operations are those functionsthat involve all the compute nodes of an operational group (132). Acollective operation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group (132) ofcompute nodes. Such an operational group (132) may include all thecompute nodes (102) in a parallel computer (100) or a subset all thecompute nodes (102). Collective operations are often built aroundpoint-to-point operations. A collective operation requires that allprocesses on all compute nodes within an operational group (132) callthe same collective operation with matching arguments. A ‘broadcast’ isan example of a collective operation for moving data among compute nodesof an operational group. A ‘reduce’ operation is an example of acollective operation that executes arithmetic or logical functions ondata distributed among the compute nodes of an operational group (132).An operational group (132) may be implemented as, for example, an MPI‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallelcommunications library, a module of computer program instructions fordata communications on parallel computers. Examples of prior-artparallel communications libraries that may be improved for topologymapping in a distributed processing system according to embodiments ofthe present invention include MPI and the ‘Parallel Virtual Machine’(‘PVM’) library. PVM was developed by the University of Tennessee, TheOak Ridge National Laboratory and Emory University. MPI is promulgatedby the MPI Forum, an open group with representatives from manyorganizations that define and maintain the MPI standard. MPI at the timeof this writing is a de facto standard for communication among computenodes running a parallel program on a distributed memory parallelcomputer. This specification sometimes uses MPI terminology for ease ofexplanation, although the use of MPI as such is not a requirement orlimitation of the present invention.

Some collective operations have a single originating or receivingprocess running on a particular compute node in an operational group(132). For example, in a ‘broadcast’ collective operation, the processon the compute node that distributes the data to all the other computenodes is an originating process. In a ‘gather’ operation, for example,the process on the compute node that received all the data from theother compute nodes is a receiving process. The compute node on whichsuch an originating or receiving process runs is referred to as alogical root.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. The interfaces forthese collective operations are defined in the MPI standards promulgatedby the MPI Forum. Algorithms for executing collective operations,however, are not defined in the MPI standards. In a broadcast operation,all processes specify the same root process, whose buffer contents willbe sent. Processes other than the root specify receive buffers. Afterthe operation, all buffers contain the message from the root process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. In a scatter operation, the logical root dividesdata on the root into segments and distributes a different segment toeach compute node in the operational group (132). In scatter operation,all processes typically specify the same receive count. The sendarguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer is divided and dispersed to all processes (including the processon the logical root). Each compute node is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0receives the first sendcount data elements from the send buffer. Rank 1receives the second sendcount data elements from the send buffer, and soon.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduction operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process' receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following predefinedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical and MPI_BAND bitwise and MPI_LOR logical or MPI_BOR bitwise orMPI_LXOR logical exclusive or MPI_BXOR bitwise exclusive or

In addition to compute nodes, the parallel computer (100) includesinput/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102)through the global combining network (106). The compute nodes (102) inthe parallel computer (100) may be partitioned into processing sets suchthat each compute node in a processing set is connected for datacommunications to the same I/O node. Each processing set, therefore, iscomposed of one I/O node and a subset of compute nodes (102). The ratiobetween the number of compute nodes to the number of I/O nodes in theentire system typically depends on the hardware configuration for theparallel computer (102). For example, in some configurations, eachprocessing set may be composed of eight compute nodes and one I/O node.In some other configurations, each processing set may be composed ofsixty-four compute nodes and one I/O node. Such example are forexplanation only, however, and not for limitation. Each I/O nodeprovides I/O services between compute nodes (102) of its processing setand a set of I/O devices. In the example of FIG. 1, the I/O nodes (110,114) are connected for data communications I/O devices (118, 120, 122)through local area network (‘LAN’) (130) implemented using high-speedEthernet.

The parallel computer (100) of FIG. 1 also includes a service node (116)coupled to the compute nodes through one of the networks (104). Servicenode (116) provides services common to pluralities of compute nodes,administering the configuration of compute nodes, loading programs intothe compute nodes, starting program execution on the compute nodes,retrieving results of program operations on the compute nodes, and soon. Service node (116) runs a service application (124) and communicateswith users (128) through a service application interface (126) that runson computer terminal (122).

The parallel computer (100) of FIG. 1 operates generally for topologymapping in a distributed processing system. Such a parallel computer(100) is typically composed of many compute nodes, but for ease ofexplanation two of the compute nodes in this example are referenced inparticular, compute node (102 a) and compute node (102 b). Each computenode includes a message passing module (101 a, 101 b). In the example ofFIG. 1, each message passing module (101 a, 101 b) may supports tasksthat exchange messages. The message passing module (101 a, 101 b) ofFIG. 1 may be embodied, for example, as computer program instructionsthat, when executed by a compute node (102 a, 102 b), facilitate theexchange of messages in a system configured for topology mapping.

In the example of FIG. 1, the parallel computer (100) carries outtopology mapping in a distributed processing system by initiating, by asending task (602), a message passing operation that is unrelated totopology mapping with a receiving task (612). In the example of FIG. 1,topology mapping is also carried out by including topologicalinformation in a message (608) generated by the message passingoperation. In the example of FIG. 1, the topological information isembodied as an identification of the rank (610) of the sending task(602) and an identification (611) of the compute node (102 a) upon whichthe sending task (602) resides. Readers will understand that topologicalinformation may also include, for example, an identification of networkconnections that are available to the sending task (602), anidentification of the type of network connections that are available tothe sending task (602), an identification of a network that includes thecompute node upon which the sending task (602) resides, anidentification of other compute nodes that are part of a network thatincludes the compute node upon which the sending task (602) resides, andso on.

In the example of FIG. 1, topology mapping is also carried out bymapping, by the receiving task (612), the topological information forthe sending task (602). Mapping such topological information may becarried out, for example, by storing the topological information inmemory available to the receiving task (612) such that the receivingtask (612) has access to information describing the topology withinwhich the sending task (602) operates.

In the example of FIG. 1, topology mapping is also carried out bydetermining whether the sending task (602) and the receiving task (612)reside on the same topological unit. In the example of FIG. 1, atopological unit is depicted as a single compute node such that thesending task (602) and the receiving task (612) reside on the sametopological unit if the sending task (602) and the receiving task (612)reside on the same compute node. In alternative embodiments, atopological unit may include multiple compute nodes that share a directdata communications connection, as multiple compute nodes that share aparticular type of connection, and so on such that the sending task(602) and the receiving task (612) are said to reside on the sametopological unit if the sending task (602) and the receiving task (612)reside on any of the compute nodes in a particular topological unit.

In the example of FIG. 1, if the sending task (602) and the receivingtask (612) reside on the same topological unit, the sending task (602)and the receiving task (612) may use an optimal local network patternfor subsequent message passing operations between the sending task (602)and the receiving task (602). In the example of FIG. 1, an optimal localnetwork pattern can include local memory on a compute node such that ifthe sending task (602) and the receiving task (612) reside on the samecompute node, the sending task (602) and the receiving task (612) mayuse local shared memory for subsequent message passing operationsbetween the sending task (602) and the receiving task (602)—rather thanusing a data communications network for subsequent message passingoperations between the sending task (602) and the receiving task (612).That is, in view of the fact that the sending task (602) and thereceiving task (612) reside on the same topological unit, the sendingtask (602) and the receiving task (612) may make use of that topologyfor subsequent message passing operations rather than using a datacommunications network as would be required by two tasks that are not onthe same topological unit. In the example of FIG. 1, if the sending task(602) and the receiving task (612) do not reside on the same topologicalunit, the sending task (602) and the receiving task (612) may use a datacommunications network between the topological unit of the sending task(602) and the topological unit of the receiving task (612) forsubsequent message passing operations between the sending task (602) andthe receiving task (612).

The arrangement of nodes, networks, and I/O devices making up theexample apparatus illustrated in FIG. 1 are for explanation only, notfor limitation of the present invention. Apparatus capable of topologymapping in a distributed processing system according to embodiments ofthe present invention may include additional nodes, networks, devices,and architectures, not shown in FIG. 1, as will occur to those of skillin the art. The parallel computer (100) in the example of FIG. 1includes sixteen compute nodes (102); parallel computers capable oftopology mapping in a distributed processing system according toembodiments of the present invention sometimes include thousands ofcompute nodes. In addition to Ethernet (174) and JTAG (104), networks insuch data processing systems may support many data communicationsprotocols including for example TCP (Transmission Control Protocol), IP(Internet Protocol), and others as will occur to those of skill in theart. Various embodiments of the present invention may be implemented ona variety of hardware platforms in addition to those illustrated in FIG.1.

Topology mapping in a distributed processing system according toembodiments of the present invention is generally implemented on aparallel computer that includes a plurality of compute nodes organizedfor collective operations through at least one data communicationsnetwork. In fact, such computers may include thousands of such computenodes. Each compute node is in turn itself a kind of computer composedof one or more computer processing cores, its own computer memory, andits own input/output adapters. For further explanation, therefore, FIG.2 sets forth a block diagram of an example compute node (102) useful ina parallel computer capable of topology mapping in a distributedprocessing system according to embodiments of the present invention. Thecompute node (102) of FIG. 2 includes a plurality of processing cores(165) as well as RAM (156). The processing cores (165) of FIG. 2 may beconfigured on one or more integrated circuit dies. Processing cores(165) are connected to RAM (156) through a high-speed memory bus (155)and through a bus adapter (194) and an extension bus (168) to othercomponents of the compute node. Stored in RAM (156) is an applicationprogram (159), a module of computer program instructions that carriesout parallel, user-level data processing using parallel algorithms.

Also stored RAM (156) is a parallel communications library (161), alibrary of computer program instructions that carry out parallelcommunications among compute nodes, including point-to-point operationsas well as collective operations. A library of parallel communicationsroutines may be developed from scratch for use in systems according toembodiments of the present invention, using a traditional programminglanguage such as the C programming language, and using traditionalprogramming methods to write parallel communications routines that sendand receive data among nodes on two independent data communicationsnetworks. Alternatively, existing prior art libraries may be improved tooperate according to embodiments of the present invention. Examples ofprior-art parallel communications libraries include the ‘Message PassingInterface’ (‘MPI’) library and the ‘Parallel Virtual Machine’ (‘PVM’)library.

In the example of FIG. 2, the parallel communications library (161)includes a message sending operation (201) and a message receivingoperation (203). In the example of FIG. 2, the message sending operation(201) may be useful for topology mapping in a distributed processingsystem as the message sending operation (201) may be used by a sendingtask to send a message that is unrelated to topology mapping with areceiving task. The message sending operation (201) may also be usefulfor topology mapping in a distributed processing system as the messagesending operation (201) may be used by a sending task to include in amessage generated by the message sending operation (201) topologicalinformation for the sending task. In the example of FIG. 2, the messagereceiving operation (203) may be useful for topology mapping in adistributed processing system as the message receiving operation (203)may be used by a receiving task to map the topological information forthe sending task and to determine whether the sending task and thereceiving task reside on the same topological unit. In such an example,if the sending task and the receiving task reside on the sametopological unit, the tasks may use an optimal local network pattern forsubsequent message passing operations between the tasks, otherwise thetasks may use a data communications network between the topological unitof the sending task and the topological unit of the receiving task forsubsequent message passing operations between the tasks.

Also stored in RAM (156) is an operating system (162), a module ofcomputer program instructions and routines for an application program'saccess to other resources of the compute node. It is typical for anapplication program and parallel communications library in a computenode of a parallel computer to run a single thread of execution with nouser login and no security issues because the thread is entitled tocomplete access to all resources of the node. The quantity andcomplexity of tasks to be performed by an operating system on a computenode in a parallel computer therefore are smaller and less complex thanthose of an operating system on a serial computer with many threadsrunning simultaneously. In addition, there is no video I/O on thecompute node (102) of FIG. 2, another factor that decreases the demandson the operating system. The operating system (162) may therefore bequite lightweight by comparison with operating systems of generalpurpose computers, a pared down version as it were, or an operatingsystem developed specifically for operations on a particular parallelcomputer. Operating systems that may usefully be improved, simplified,for use in a compute node include UNIX™, Linux™, Windows XP™, AIX™,IBM's i5/OS™, and others as will occur to those of skill in the art.

The example compute node (102) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters useful in apparatus useful for topology mappingin a distributed processing system include modems for wiredcommunications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (102)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 include a JTAGSlave circuit (176) that couples example compute node (102) for datacommunications to a JTAG Master circuit (178). JTAG is the usual nameused for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also useful asa mechanism for debugging embedded systems, providing a convenient “backdoor” into the system. The example compute node of FIG. 2 may be allthree of these: It typically includes one or more integrated circuitsinstalled on a printed circuit board and may be implemented as anembedded system having its own processing core, its own memory, and itsown I/O capability. JTAG boundary scans through JTAG Slave (176) mayefficiently configure processing core registers and memory in computenode (102) for use in dynamically reassigning a connected node to ablock of compute nodes useful in systems for topology mapping in adistributed processing system according to embodiments of the presentinvention.

The data communications adapters in the example of FIG. 2 include aPoint-To-Point Network Adapter (180) that couples example compute node(102) for data communications to a network (108) that is optimal forpoint-to-point message passing operations such as, for example, anetwork configured as a three-dimensional torus or mesh. ThePoint-To-Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186).

The data communications adapters in the example of FIG. 2 include aGlobal Combining Network Adapter (188) that couples example compute node(102) for data communications to a global combining network (106) thatis optimal for collective message passing operations such as, forexample, a network configured as a binary tree. The Global CombiningNetwork Adapter (188) provides data communications through threebidirectional links for each global combining network (106) that theGlobal Combining Network Adapter (188) supports. In the example of FIG.2, the Global Combining Network Adapter (188) provides datacommunications through three bidirectional links for global combiningnetwork (106): two to children nodes (190) and one to a parent node(192).

The example compute node (102) includes multiple arithmetic logic units(‘ALUs’). Each processing core (165) includes an ALU (166), and aseparate ALU (170) is dedicated to the exclusive use of the GlobalCombining Network Adapter (188) for use in performing the arithmetic andlogical functions of reduction operations, including an allreduceoperation. Computer program instructions of a reduction routine in aparallel communications library (161) may latch an instruction for anarithmetic or logical function into an instruction register (169). Whenthe arithmetic or logical function of a reduction operation is a ‘sum’or a ‘logical OR,’ for example, the collective operations adapter (188)may execute the arithmetic or logical operation by use of the ALU (166)in the processing core (165) or, typically much faster, by use of thededicated ALU (170) using data provided by the nodes (190, 192) on theglobal combining network (106) and data provided by processing cores(165) on the compute node (102).

Often when performing arithmetic operations in the global combiningnetwork adapter (188), however, the global combining network adapter(188) only serves to combine data received from the children nodes (190)and pass the result up the network (106) to the parent node (192).Similarly, the global combining network adapter (188) may only serve totransmit data received from the parent node (192) and pass the data downthe network (106) to the children nodes (190). That is, none of theprocessing cores (165) on the compute node (102) contribute data thatalters the output of ALU (170), which is then passed up or down theglobal combining network (106). Because the ALU (170) typically does notoutput any data onto the network (106) until the ALU (170) receivesinput from one of the processing cores (165), a processing core (165)may inject the identity element into the dedicated ALU (170) for theparticular arithmetic operation being perform in the ALU (170) in orderto prevent alteration of the output of the ALU (170). Injecting theidentity element into the ALU, however, often consumes numerousprocessing cycles. To further enhance performance in such cases, theexample compute node (102) includes dedicated hardware (171) forinjecting identity elements into the ALU (170) to reduce the amount ofprocessing core resources required to prevent alteration of the ALUoutput. The dedicated hardware (171) injects an identity element thatcorresponds to the particular arithmetic operation performed by the ALU.For example, when the global combining network adapter (188) performs abitwise OR on the data received from the children nodes (190), dedicatedhardware (171) may inject zeros into the ALU (170) to improveperformance throughout the global combining network (106).

For further explanation, FIG. 3A sets forth a block diagram of anexample Point-To-Point Adapter (180) useful in systems for topologymapping in a distributed processing system according to embodiments ofthe present invention. The Point-To-Point Adapter (180) is designed foruse in a data communications network optimized for point-to-pointoperations, a network that organizes compute nodes in athree-dimensional torus or mesh. The Point-To-Point Adapter (180) in theexample of FIG. 3A provides data communication along an x-axis throughfour unidirectional data communications links, to and from the next nodein the −x direction (182) and to and from the next node in the +xdirection (181). The Point-To-Point Adapter (180) of FIG. 3A alsoprovides data communication along a y-axis through four unidirectionaldata communications links, to and from the next node in the −y direction(184) and to and from the next node in the +y direction (183). ThePoint-To-Point Adapter (180) of FIG. 3A also provides data communicationalong a z-axis through four unidirectional data communications links, toand from the next node in the −z direction (186) and to and from thenext node in the +z direction (185).

For further explanation, FIG. 3B sets forth a block diagram of anexample Global Combining Network Adapter (188) useful in systems fortopology mapping in a distributed processing system according toembodiments of the present invention. The Global Combining NetworkAdapter (188) is designed for use in a network optimized for collectiveoperations, a network that organizes compute nodes of a parallelcomputer in a binary tree. The Global Combining Network Adapter (188) inthe example of FIG. 3B provides data communication to and from childrennodes of a global combining network through four unidirectional datacommunications links (190), and also provides data communication to andfrom a parent node of the global combining network through twounidirectional data communications links (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in systems capable of topology mappingin a distributed processing system according to embodiments of thepresent invention. In the example of FIG. 4, dots represent computenodes (102) of a parallel computer, and the dotted lines between thedots represent data communications links (103) between compute nodes.The data communications links are implemented with point-to-point datacommunications adapters similar to the one illustrated for example inFIG. 3A, with data communications links on three axis, x, y, and z, andto and fro in six directions +x (181), −x (182), +y (183), −y (184), +z(185), and −z (186). The links and compute nodes are organized by thisdata communications network optimized for point-to-point operations intoa three dimensional mesh (105). The mesh (105) has wrap-around links oneach axis that connect the outermost compute nodes in the mesh (105) onopposite sides of the mesh (105). These wrap-around links form a torus(107). Each compute node in the torus has a location in the torus thatis uniquely specified by a set of x, y, z coordinates. Readers will notethat the wrap-around links in the y and z directions have been omittedfor clarity, but are configured in a similar manner to the wrap-aroundlink illustrated in the x direction. For clarity of explanation, thedata communications network of FIG. 4 is illustrated with only 27compute nodes, but readers will recognize that a data communicationsnetwork optimized for point-to-point operations for use in topologymapping in a distributed processing system in accordance withembodiments of the present invention may contain only a few computenodes or may contain thousands of compute nodes. For ease ofexplanation, the data communications network of FIG. 4 is illustratedwith only three dimensions, but readers will recognize that a datacommunications network optimized for point-to-point operations for usein topology mapping in a distributed processing system in accordancewith embodiments of the present invention may in facet be implemented intwo dimensions, four dimensions, five dimensions, and so on. Severalsupercomputers now use five dimensional mesh or torus networks,including, for example, IBM's Blue Gene Q™.

For further explanation, FIG. 5 sets forth a line drawing illustratingan example global combining network (106) useful in systems capable oftopology mapping in a distributed processing system according toembodiments of the present invention. The example data communicationsnetwork of FIG. 5 includes data communications links (103) connected tothe compute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with global combining network adapters similar tothe one illustrated for example in FIG. 3B, with each node typicallyproviding data communications to and from two children nodes and datacommunications to and from a parent node, with some exceptions. Nodes inthe global combining network (106) may be characterized as a physicalroot node (202), branch nodes (204), and leaf nodes (206). The physicalroot (202) has two children but no parent and is so called because thephysical root node (202) is the node physically configured at the top ofthe binary tree. The leaf nodes (206) each has a parent, but leaf nodeshave no children. The branch nodes (204) each has both a parent and twochildren. The links and compute nodes are thereby organized by this datacommunications network optimized for collective operations into a binarytree (106). For clarity of explanation, the data communications networkof FIG. 5 is illustrated with only 31 compute nodes, but readers willrecognize that a global combining network (106) optimized for collectiveoperations for use in topology mapping in a distributed processingsystem in accordance with embodiments of the present invention maycontain only a few compute nodes or may contain thousands of computenodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifies atask or process that is executing a parallel operation according toembodiments of the present invention. Using the rank to identify a nodeassumes that only one such task is executing on each node. To the extentthat more than one participating task executes on a single node, therank identifies the task as such rather than the node. A rank uniquelyidentifies a task's location in the tree network for use in bothpoint-to-point and collective operations in the tree network. The ranksin this example are assigned as integers beginning with 0 assigned tothe root tasks or root node (202), 1 assigned to the first node in thesecond layer of the tree, 2 assigned to the second node in the secondlayer of the tree, 3 assigned to the first node in the third layer ofthe tree, 4 assigned to the second node in the third layer of the tree,and so on. For ease of illustration, only the ranks of the first threelayers of the tree are shown here, but all compute nodes in the treenetwork are assigned a unique rank.

For further explanation, FIG. 6 sets forth a flow chart illustrating anexample method for topology mapping in a distributed processing system(600) according to embodiments of the present invention. FIG. 6represents a specific embodiment in which topological informationexchanged between two tasks (602, 612) includes only a rank (610) of thesending task (612) and identification (611) of the compute node that thesending task (612) resides upon. In the specific embodiment described inFIG. 6, a topological unit is depicted as a compute node (102 a) suchthat two tasks (602, 612) that reside on the same compute node (102 a)are said to reside upon the same topological unit—while two tasks thatreside on different compute nodes (102 a, 102 b, 102 c, 102 d) are saidto reside upon the different topological units. In the specificembodiment described in FIG. 6, an optimal local network pattern isdepicted as local memory (632) on a compute node (102 a). That is, localmemory (632) on a compute node (102 a) represents computer storage thatis available to a sending task (602) and a receiving task (612) thatreside on the same compute node (i.e., the same topological unit in thisembodiment), such that a sending task (602) and a receiving task (612)that reside on the same compute node (102 a) can use the memory (632) onthat compute node (102 a) to exchange messages, rather than transmittingmessages over a data communications network (630) that is external toeach task (602, 612).

In the example of FIG. 6, the distributed processing system (600)includes a plurality of compute nodes (102 a, 102 b, 102 c, 102 d). Eachcompute node (102 a, 102 b, 102 c, 102 d) in the example of FIG. 6 has aplurality of tasks (602, 612, 632 b, 632 c, 632 d), each of which isassigned a unique rank. In the example of FIG. 6, the rank serves as anidentifier for a particular task (602, 612, 632 b, 632 c, 632 d) as eachrank is unique.

The example of FIG. 6 includes initiating (606), by a sending task(602), a message passing operation (604) that is unrelated to topologymapping with a receiving task (612). In the example of FIG. 6, a messagepassing operation (604) that is unrelated to topology mapping with areceiving task (612) may be embodied, for example, as computer programinstructions that, when executed, cause a message to be passed from afirst task operating on a compute node to a second task operating on acompute node. A message passing operation (604) that is unrelated totopology mapping with a receiving task (612) may include, for example,message passing operations available through MPI as described above.

The example of FIG. 6 also includes including (607), by the sending task(602), in a message (608) generated by the message passing operation(604) an identification of the rank (610) of the sending task (602) andidentification (611) of the compute node (102 a) upon which the sendingtask (602) resides. In the example of FIG. 6, including (607) anidentification of the rank (610) of the sending task (602) and anidentification (611) of the compute node (102 a) upon which the sendingtask (602) resides in a message (608) generated by the message passingoperation (604) may be carried out, for example, by including theidentification of the rank (610) of the sending task (602) and theidentification (611) of the compute node (102 a) upon which the sendingtask (602) resides as parameters in the message (608), including suchinformation in a header of the message (608), and so on.

In the example of FIG. 6, including (607) topological information in amessage (608) generated by the message passing operation (604) may occuronly when the receiving task (612) does not have possession oftopological information for the sending task (602). That is, rather thansending topological information multiple times to a receiving task (612)that is already aware of the topology within which the sending task(602) operates, the sending task (602) may only include topologicalinformation in messages when it is beneficial to do so. For example, thesending task (602) may include topological information in a message(608) only the first time that the sending task (602) sends a message(608) to the receiving task (612). Alternatively, the sending task (602)may include topological information in a message (608) to the receivingtask (612) at predetermined intervals, upon request, a maximum numbertimes, and so on.

The example of FIG. 6 also includes mapping (614), by the receiving task(612), the rank (610) of the sending task (602) and the identification(611) of the compute node (102 a) upon which the sending task (602)resides. In the example of FIG. 6, mapping (614) the rank (610) of thesending task (602) and the identification (611) of the compute node (102a) upon which the sending task (602) resides may be carried out, forexample, by creating an entry in a task topology table for the receivingtask (612). Table 1 illustrates an example of a task topology table:

TABLE 1 Task Topology Table Task Rank Compute Node ID 1 102a 2 102c 3102d 4 102d 5 102b 6 102a

In the task topology table illustrated in table 1, there are six entriesrepresenting six tasks. The six entries indicate that tasks with ranksof ‘1’ and ‘6’ are operating on compute node (102 a), a task with a rankof ‘5’ is operating on compute node (102 b), a task with a rank of ‘2’is operating on compute node (102 c), and tasks with ranks of ‘3’ and‘4’ are operating on compute node (102 d). In such an example, becausethe receiving task (612) is operating on compute node (102 a), taskswith ranks of ‘1’ and ‘6’ are operating on the same compute node (102 a)as the receiving task (612) while all other tasks in the task topologytable are operating on compute nodes (102 b, 102 c, 102 d) other thanthe compute node (102 a) upon which the receiving task (612) resides.

The example of FIG. 6 also includes determining (616) whether thesending task (602) and the receiving task (612) reside on the samecompute node (102 a). In the example of FIG. 6, determining (616)whether the sending task (602) and the receiving task (612) reside onthe same compute node (102 a) may be carried out, for example, bysearching a task topology table to determine whether the sending task(602) resides on the same compute node (102 a) as the receiving task(612). As described above, the message (608) generated by sending task(602) includes the identification of the rank (610) of the sending task(602) and the identification (611) of the compute node (102 a) uponwhich the sending task (602) resides. This information may be used bythe receiving task (612), along with information in the task topologytable, to determine whether the sending task (602) and the receivingtask (612) reside on the same compute node (102 a).

The example of FIG. 6 includes using local shared memory (632) forsubsequent message passing operations between the sending task (602) andthe receiving task (612) if the sending task (602) and the receivingtask (612) do (618) reside on the same compute node (102 a). In theexample of FIG. 6, the local shared memory (632) may be embodied, forexample, as memory (632) in the compute node (102 a) that is utilized,for example, as a messaging queue. Subsequent message passing operationsbetween the sending task (602) and the receiving task (612) maytherefore be carried out by inserting messages into the messaging queuesuch that messages can be exchanged by the sending task (602) and thereceiving task (612) without the message ever being transmitted over anetwork.

The example of FIG. 6 includes using a data communications network (630)between the compute nodes (102 a, 102 b, 102 c, 102 d) for subsequentmessage passing operations between the sending task (602) and thereceiving task (612) if the sending task (602) and the receiving task(6012) do not (620) reside on the same compute node (102 a). In theexample of FIG. 6, the data communications network (630) may beembodied, for example, as a point-to-point network, a global combiningnetwork, and so on as described above.

For further explanation, FIG. 7 sets forth a flow chart illustrating afurther example method for topology mapping in a distributed processingsystem (600) according to embodiments of the present invention. Theexample of FIG. 7 is similar to the example of FIG. 6 as it alsoincludes:

-   -   initiating (606), by a sending task (602), a message passing        operation (604) that is unrelated to topology mapping with a        receiving task (612),    -   including (607), by the sending task (602), in a message (704)        generated by the message passing operation (604) an        identification of the rank (610) of the sending task (602) and        an identification (611) of the compute node (102 a) upon which        the sending task (602) resides,    -   mapping (614), by the receiving task (612), the rank (610) of        the sending task (602) and the identification (611) of the        compute node (102 a) upon which the sending task (602) resides,    -   determining (616) whether the sending task (602) and the        receiving task (612) reside on the same compute node (102 a),    -   if the sending task (602) and the receiving task (612) do (618)        reside on the same compute node (102 a), using (622) local        shared memory (632) for subsequent message passing operations        between the sending task (602) and the receiving task (612), and    -   if the sending task (602) and the receiving task (612) do not        (620) reside on the same compute node (102 a), using (624) a        data communications network (630) between the compute nodes (102        a, 102 b, 102 c, 102 d) for subsequent message passing        operations between the sending task (602) and the receiving task        (612).

The example of FIG. 7 also includes including (702), by the sending task(602), in the message (704) generated by the message passing operation(604), compute node configuration parameters (706) for the compute node(102 a) upon which the sending task (602) resides. In the example ofFIG. 7, the configuration parameters (706) for the compute node (102 a)upon which the sending task (602) resides can provide more refinedadditional information about the compute node (102 a) upon which thesending task (602) resides such as, for example, a listing of networkresources of the compute node (102 a), addressing information for thecompute node (102 a), and so on.

In the example of FIG. 7, the message passing operation (604) generatesa point-to-point message (704). In the example of FIG. 7, apoint-to-point message (704) may be transmitted using a point-to-pointadapter as described above with reference to FIG. 3A. Such messages maybe transmitted using a torus network topology as described above withreference to FIG. 4.

For further explanation, FIG. 8 sets forth a flow chart illustrating afurther example method for topology mapping in a distributed processingsystem (600) according to embodiments of the present invention. Theexample of FIG. 8 is similar to the example of FIG. 6 as it alsoincludes:

-   -   initiating (606), by a sending task (602), a message passing        operation (604) that is unrelated to topology mapping with a        receiving task (612),    -   including (607), by the sending task (602), in a message (704)        generated by the message passing operation (604) an        identification of the rank (610) of the sending task (602) and        an identification (611) of the compute node (102 a) upon which        the sending task (602) resides,    -   mapping (614), by the receiving task (612), the rank (610) of        the sending task (602) and the identification (611) of the        compute node (102 a) upon which the sending task (602) resides,    -   determining (616) whether the sending task (602) and the        receiving task (612) reside on the same compute node (102 a),    -   if the sending task (602) and the receiving task (612) do (618)        reside on the same compute node (102 a), using (622) local        shared memory (632) for subsequent message passing operations        between the sending task (602) and the receiving task (612), and    -   if the sending task (602) and the receiving task (612) do not        (620) reside on the same compute node (102 a), using (624) a        data communications network (630) between the compute nodes (102        a, 102 b, 102 c, 102 d) for subsequent message passing        operations between the sending task (602) and the receiving task        (612).

In the example of FIG. 8, however, the message passing operation (604)is a collective operation. As described above, collective operations arethose functions that involve all the compute nodes of an operationalgroup. A collective operation is an operation, a message-passingcomputer program instruction that is executed simultaneously, that is,at approximately the same time, by all the compute nodes in anoperational group of compute nodes.

In the example of FIG. 8, also includes initiating (606), by a sendingtask (602), a message passing operation (604) that is unrelated totopology mapping with a receiving task (612) includes initiating (802),by each task (602, 612), a collective operation unrelated to topologymapping. Because collective operations are executed at approximately thesame time by all the compute nodes in an operational group of computenodes, initiating (606) a message passing operation (604) that is acollective operation by one task, such as the sending task (602),requires that all other tasks, such as the receiving task (612) alsoinitiate the same collective operation if each task (602, 612) is partof the same operational group.

FIGS. 6-8 represent specific embodiments in which topologicalinformation exchanged between two tasks includes only a rank of thesending task and an identification of the compute node that the sendingtask resides upon. Readers will appreciate, however, that topologicalinformation exchanged between two tasks may include other informationdescribing the topology that each task operates within. For example,topological information may include information describing the datacommunication connections that are available to the task, anidentification of the compute nodes that task can communicate withdirectly, an identification of the types of data communicationconnections that are available to the task, and so on.

In the specific embodiments described in FIGS. 6-8, a topological unitis depicted as a compute node (102 a). Readers will appreciate that atopological unit may be embodied, for example, as a collection ofcompute nodes that share a direct data communications link with eachother, a collection of compute nodes that share a particular type ofdata communications link with each other, and so on. In the specificembodiments described in FIGS. 6-8, an optimal local network pattern isdepicted as being shared memory on a single compute node. Readers willappreciate that an optimal local network pattern may be embodied, forexample, as shared memory between two or more compute nodes, as a directdata communications link between two nodes, as a particular type of datacommunications link between two nodes, and so on.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

What is claimed is:
 1. A method of topology mapping in a distributedprocessing system, the distributed processing system including aplurality of compute nodes, each compute node having a plurality oftasks, the method comprising: initiating, by a sending task, a messagepassing operation that is unrelated to topology mapping with a receivingtask, wherein the message passing operation generates a message thatincludes topological information for the sending task, wherein thetopological information comprises a task rank for the sending task andan identifier for a topological unit upon which the sending taskresides; mapping, by the receiving task, the topological information forthe sending task wherein mapping the topological information for thesending task comprises creating an association between the task rank forthe sending task and the identifier for a topological unit upon whichthe sending task resides; determining that the sending task and thereceiving task do not reside on the same topological unit using anidentifier for a topological unit upon which the receiving task residesand the association between the task rank for the sending task and theidentifier for the topological unit upon which the sending task resides;and in response to the determination that the sending task and thereceiving task do not reside on the same topological unit, using a datacommunications network between the topological unit of the sending taskand the topological unit of the receiving task for subsequent messagepassing operations between the sending task and the receiving task. 2.The method of claim 1 further comprising including, by the sending task,in the message generated by the message passing operation, topologicalunit configuration parameters for the topological unit upon which thesending task resides.
 3. The method of claim 1 wherein the messagepassing operation generates a point-to-point message.
 4. The method ofclaim 1 wherein the message passing operation is a collective operationand initiating, by a sending task, a message passing operation that isunrelated to topology mapping with a receiving task further comprisesinitiating, by each task, a collective operation unrelated to topologymapping.
 5. The method of claim 1 wherein including, by the sendingtask, in a message generated by the message passing operationtopological information for the sending task occurs only when thereceiving task does not have possession of topological information forthe sending task.
 6. The method of claim 1 wherein the optimal localnetwork pattern is shared memory.
 7. The method of claim 2, wherein theconfiguration parameters comprise a listing of network resources of thetopological unit upon which the sending task resides and addressinginformation for the topological unit upon which the sending taskresides.